Constructing stability: optimal learning in noisy ecological niches.


Journal

Proceedings. Biological sciences
ISSN: 1471-2954
Titre abrégé: Proc Biol Sci
Pays: England
ID NLM: 101245157

Informations de publication

Date de publication:
Oct 2024
Historique:
medline: 30 10 2024
pubmed: 30 10 2024
entrez: 29 10 2024
Statut: ppublish

Résumé

Organisms can learn in response to environmental inputs as well as actively modify their environments through niche construction on slower evolutionary time scales. How quickly should an organism respond to a changing environment, and when possible, should organisms adjust the time scale of environmental change? We formulate these questions using a model of learning costs that considers optimal time scales of both memory and environment. We derive a general, sublinear scaling law for optimal memory as a function of environmental persistence. This encapsulates a trade-off between remembering and forgetting. We place learning strategies within a niche construction dynamics in a game theoretic setting. Niche construction is found to reduce or stabilize environmental volatility when learned environmental resources can be monopolized. When learned resources are shared, niche destructors evolve to degrade the shared environment. We integrate these results into a metabolic scaling framework in order to derive learning strategies as a function of body size.

Identifiants

pubmed: 39471866
doi: 10.1098/rspb.2024.1606
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

20241606

Subventions

Organisme : Austrian Science Fund
Organisme : Omega Miller Fund
Organisme : James S. McDonnell Foundation
Organisme : TWCF
Pays : United States
Organisme : Robert Wood Johnson Foundation
ID : 81366

Auteurs

Edward D Lee (ED)

Complexity Science Hub, Josefstædter Strasse 39, Vienna 1080, Austria.

Jessica C Flack (JC)

Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA.

David C Krakauer (DC)

Santa Fe Institute, 1399 Hyde Park Rd, Santa Fe, NM 87501, USA.

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Classifications MeSH